Raising the question of what and how metabolic syndrome is covered through the renowned online press, hence, this study attempted to determine the ways in which the newspapers selects chronic diseases and how the syndrome informs the public of causation and adverse outcome. Web-based news platforms from four leading newspapers in mainland China were sampled for machine computational analysis. Two prominent metabolic diseases, its causation and causal inference were identified in a total of 16,005 articles in the past 10 years. Descriptive statistics is reported while a word cloud displays the entire news content as a graphical representation of word frequency with recurring expressions. A dendograms was summarized to provide a richer context to interpret association between metabolic syndrome and causal inference to verify if misinformation was spread through the renowned online press. Results indicate that news stories tend to provide equivocal descriptions of metabolic disease burden while implying explicitly that alcohol, tobacco, or genes might be the main cause of adverse outcomes for metabolic syndrome. This study emphasized the importance of taxonomy of coding, causal assessments of news stories to inform debates on addressing causal inferences, message design, and unique frames in the media content. It concludes with limitation of datamining and directions for future research.